Biogeographical and analytical implications of temporal variability in geographically diverse soundscapes. (May 2020)

• Record Type: Journal Article
• Authors: Francomano, Dante; Gottesman, Benjamin L.; Pijanowski, Bryan C.
• Is Part Of: Ecological indicators. Volume 112(2020)
• Publication Date: 2020-05
• Subjects: Soundscape ecology -- Ecoacoustics -- Acoustic monitoring -- Temporal variability -- Study design; ACI Acoustic complexity index -- AR Autocorrelation range -- BI Bioacoustic index -- CGS Center for Global Soundscapes -- DF Dante Francomano -- Hf Spectral entropy -- NS Number of subdivisions -- PAM Passive acoustic monitoring -- SD Standard deviation -- TAT Total analysis time -- WA Wildlife Acoustics; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2019.105845
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Continuous soundscape recordings were made in eight ecosystems on four continents. Temporal variability of soundscapes was quantified using two distinct measures. Daytime exhibited high temporal variability relative to dusk and nighttime. Increasing evenness of subsampling distribution reduces error in representation. Marginal precision gains diminish substantially beyond 1/3 recording time. Abstract: Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems. Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables. Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes. We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions drastically increased the representativeness of a sampling scheme, while increases in subsample duration yielded fairly minimal gains in representativeness between 33 and 67% of the full time one wishes to represent. Generally, our results show that for a long-term, fine temporal resolution monitoring program, one should record in evenly distributed durations at least as short as 1 min while only recording up to a third of the time one wishes to represent. While more continuous monitoring can be advantageous and necessary in many cases, current economic and logistical limitations in power, data storage, and analysis capabilities will often warrant optimized subsampling designs.